Peroxisomal Lactate Dehydrogenase Is Generated by Translational Readthrough in Mammals

1 Peroxisomal lactate dehydrogenase is generated 2 by translational readthrough in mammals 3 4 Fabian Schueren1†, Thomas Lingner2†, Rosemol George1†, Julia Hofhuis1, 5 Corinna Dickel1, Jutta Gärtner1*, Sven Thoms1* 6 7 1Department of Pediatrics and Adolescent Medicine, University Medical Center, 8 Georg-August-University Göttingen, Germany 9 2Department of Bioinformatics, Institute for Microbiology and Genetics, 10 Georg-August-University Göttingen, Germany 11 † Co-first author 12 13 * Correpondence: [email protected] (J.G.); [email protected] 14 (S.T.) 15 16 Competing interest statement: The authors declare no competing interest. 17 18 ABSTRACT 19 Translational readthrough gives rise to low abundance proteins with C-terminal 20 extensions beyond the stop codon. To identify functional translational readthrough, we 21 estimated the readthrough propensity (RTP) of all stop codon contexts of the human 22 genome by a new regression model in silico, identified a nucleotide consensus motif for 23 high RTP by using this model, and analyzed all readthrough extensions in silico with a 24 new predictor for peroxisomal targeting signal type 1 (PTS1). Lactate dehydrogenase B 25 (LDHB) showed the highest combined RTP and PTS1 probability. Experimentally we 26 show that at least 1.6% of the total cellular LDHB getting targeted to the peroxisome by 27 a conserved hidden PTS1. The readthrough-extended lactate dehydrogenase subunit 28 LDHBx can also co-import LDHA, the other LDH subunit into peroxisomes. Peroxisomal 29 LDH is conserved in mammals and likely contributes to redox equivalent regeneration in 30 peroxisomes. 31 1 32 33 INTRODUCTION 34 Translation of genetic information encoded in mRNAs into proteins is carried out by ribosomes. 35 When a stop codon enters the ribosomal A site release factors bind the stop codon, hydrolyze 36 the peptidyl-tRNA bond and trigger the release of the polypeptide from the ribosome. If instead 37 of release factor 1 (eRF1) a near-cognate aminoacyl-tRNA pairs with the stop codon in the 38 ribosomal A site, the stop signal is suppressed. Such decoding of a stop codon as a sense 39 codon is known as translational readthrough. As a consequence, translation ensues to the next 40 stop codon resulting in the synthesis of C-terminally extended proteins (Baranov et al., 2002; 41 Firth and Brierley, 2012; Namy et al., 2004). Mutant tRNAs are classical stop codon 42 suppressors, but also in normal physiology termination occurs with less than 100% efficiency. 43 A number of cis-elements on the mRNA, typically 3’ of the stop together with trans-acting factors 44 are known to influence stop codon readthrough (Firth et al., 2011). A case of translational 45 readthrough dependent on RNA cis-elements has recently been found and termed programmed 46 translational readthrough (PTR) (Eswarappa et al., 2014). But it is also known that the stop 47 codon itself and the nucleotides before and after the stop codon affect readthrough. The three 48 stop codons differ in their tendency to be suppressed. In human UAA is least and UGA is most 49 likely to allow readthrough (Baranov et al., 2002; Beier and Grimm, 2001). Studies also show 50 that the nucleotide immediately downstream of the stop codon is biased and can strongly 51 influence readthrough (McCaughan et al., 1995). We here define translational readthrough that 52 is entirely dependent on the stop codon and the nucleotides in its immediate vicinity as basal 53 translational readthrough (BTR). Thus BTR is independent of cis-acting elements and also 54 differs from pharmacologically induced readthrough. Induction of readthrough, most prominently 55 by aminoglycoside antibiotics is an attractive strategy in the treatment of the large number of 56 genetic disorders caused by premature stop codons (Bidou et al., 2012; Keeling et al., 2014). 57 In viruses, readthrough optimizes the coding capacity of compact genomes (Firth and Brierley, 58 2012). In the yeast S. cerevisiae, the eukaryotic release factor eRF3 can form prion-like 59 polymers which introduces a level of epigenetic regulation not found in other eukaryotes (Tuite 60 and Cox, 2003). In fungi, translational readthrough extends cytosolic glycolytic enzymes by a 61 cryptic peroxisomal targeting signal (Freitag et al., 2012). In Drosophila, readthrough is known to 62 affect between 200 and 300 proteins (Dunn et al., 2013; Jungreis et al., 2011), and in mammals 63 readthrough has been described for more than 50 individual transcripts (Chittum et al., 1998; 64 Dunn et al., 2013; Eswarappa et al., 2014; Geller and Rich, 1980; Loughran et al., 2014; 65 Yamaguchi et al., 2012). Ribosome profiling and phylogenetic approaches provide powerful 2 66 methods for the systematic identification of readthrough in mammals (Dunn et al., 2013; 67 Eswarappa et al., 2014; Jungreis et al., 2011; Loughran et al., 2014). 68 We wanted to uncover a physiological role of translational readthrough in humans by identifying 69 C-terminal extensions with targeting signals that would create a functional difference between 70 the normal and the readthrough-extended form. To achieve this aim, we concentrated on 71 proteins deriving from BTR. Based on experimental data, we assigned regression coefficients to 72 all possible nucleotides in the stop codon context and, using those regression coefficients, 73 estimated the readthrough propensity (RTP) of all stop codons in the human genome or 74 transcriptome. We were able to formally derive a new nucleotide consensus for high RTP from 75 the regression coefficients of our model. Then we screened all predicted C-terminal extensions 76 for peroxisomal targeting signals because peroxisomes import most of their matrix proteins 77 through a short targeting signal (PTS1) at the very C-terminus (Smith and Aitchison, 2013). We 78 here show that LDHB combines a very high translational readthrough with a hidden, yet 79 functional and evolutionarily conserved PTS1. This peroxisomal isoform of LDH, containing the 80 readthrough-extended LDHBx subunit, is likely to be involved in the regeneration of redox 81 equivalents for peroxisomal β-oxidation. 82 83 84 RESULTS 85 Genome-wide in silico analysis of basal translational readthrough 86 To develop a computational assessment of the RTP of all human stop codon contexts (SCCs) 87 that would allow the identification of genes with high BTR, we focused on SCCs comprising 15 88 nucleotides including and surrounding the stop codon (nucleotides -6 to +9, stop codon at 89 positions 1 to 3). To be able to calculate a linear regression between the SCCs and their 90 experimental BTR values, we formalized SCCs using a binary vector that represented the stop 91 context in a multi-dimensional vector space (Figure 1A and Figure 1 – figure supplement 1). The 92 three stop codons were condensed into one position, so that the binary vector required 51 93 dimensions, for the four possible nucleotides in the six positions before and after the stop codon, 94 and for the three stop codons (12*4+3). The vector was combined with experimentally 95 accessible BTR frequencies. For the first approximation model (LIN) we used 66 sequences 96 derived from human nonsense mutations (Floquet et al., 2012). The nucleotide sequences of 97 these stop contexts show no bias with respect to RTP, because the contexts and the stop 98 codons evolved independently, therefore the context nucleotides are random in relation to the 99 stop codon. We calculated a linear regression model for these SCCs and used only the 100 experimental BTR values that had been measured in the absence of aminoglycosides. The 3 101 model assigns regression coefficients to all possible nucleotides in the stop context (Figure 1 – 102 figure supplement 1). 103 For a first round of whole-genome RTP prediction, we extracted the SCCs for each transcript 104 from the Ensembl database and calculated RTP by adding up the regression coefficients of all 105 relevant positions. An outline of this algorithm is shown in Figure 1A and in more detail in Figure 106 1 – figure supplement 1. A sortable list of LIN RTP values for all human transcripts is contained 107 in Dataset 1 (Schueren et al., 2014). 108 To expand the data basis of the RTP algorithm and to obtain evidence that the algorithm indeed 109 predicts BTR values, we selected candidate transcripts with high, intermediate, and low RTP 110 and tested them by a dual reporter assay (Figure 1B and Table 1). For experimental analysis, 111 SCCs spanning 10 nucleotides upstream and downstream of the stop codon were expressed 112 with a 5’/N-terminal yellow fluorescent protein (Venus) and a 3’/C-terminal humanized Renilla 113 luciferase (hRluc) tag. Stop suppression leads to the expression of hRluc, Venus served as an 114 internal expression control. Readthrough is expressed as luciferase activity per Venus 115 fluorescence. This approach excludes introns and exon junction complexes and, due to the 116 relatively short stretch of variable nucleotides between the reporters, also does not allow for 117 extensive RNA structures that could modulate readthrough. In consequence, this form of the 118 dual reporter assay focusses on the assessment of BTR without influence by specific cis- 119 elements. The additional candidates tested showed BTR between 0.10 (±0.006) and 2.91 120 (±0.15) % relative to the 100% readthrough control expressing the Venus-hRluc fusion protein 121 without intervening stop codon region (Figure 1C and Table 1). Induction of readthrough by the 122 aminoglycoside antibiotic geneticin (G418) was between 3.25 (±0.41) and 40.38 (±5.33) -fold 123 (Figure 1C). The Geneticin could only increase the luciferase-per-Venus signal when a stop 124 codon separated Venus and luciferase, indicating that our dual reporter assay faithfully reports 125 readthrough. The finding that experimental readthrough could be increased by treatment with 126 aminoglycosides also excludes alternative mechanisms such as RNA editing or splicing that 127 could be considered to explain the relative increase of the luciferase over the Venus signal.

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